Follow the instructions below and learn how to use FetGOat.

Enter your email address. Once FetGOat finalized the analysis, a ZIP archive containing tabular and graphical representations of the results will be sent to that email address.

Upload a file listing identifiers of genes/proteins. The maximum number of identifiers is limited to 3000. For each strain, a number of different identifier formats can be submitted. If the identifiers that you have chosen are not supported by FetGOat, you will receive an error message. You can either try to use a different identifier format or ask bmnitsche@gmail.com to extend the list of supported identifier formats. Download here a compressed file containing an example file with identifiers (A. niger strain CBS 513.88) of roughly 700 maltose-induced genes (Jørgensen et al. 2010).

Choose the corresponding Aspergillus strain.

Define the significance threshold. The given value corresponds to the probability that the enrichment results were obtained by chance.

Choose the ontology for which you would like to perform enrichment analysis. Independent of the chosen ontologies, the correction for multiple hypothesis testing will allways be performed separately for each ontology.

Select, if you would like to identify over and/or under-represented GO terms.

Choose the minimal annotation group size. Only GO terms with an equal or grater number of annotated genes/proteins will be considered for enrichment analysis.

It's possible to either enable or disable correction for multiple hypothesis testing. We strongly recommend to correct for multiple hypothesis testing. Currently, only the Benjamini & Hochberg correction method is implemented.

For the graphical representation of the enrichment results, it is possible to either choose a rooted or non-rooted layout. In the rooted layout, all enriched GO terms are directly or indirectly linked with the corresponding ontology (Biological Process, Cellular Component or Molecular Function), (see example graph below).

Submit the job.

Fisher's exact test is calculated from the following counts (given in the results tables):

sP: number of genes/proteins within the set of interest that are annotated by the corresponding GO term

rP: number of all remaining (outside the set of interest) genes/proteins that are annotated by the corresponding GO term

sN: number of GO annotated genes/proteins within the set of interest that are not annotated by the corresponding GO term

rN: number GO annotated genes/proteins that are not contained in the set of interest and that are not annotated by the corresponding GO term